Genome-wide genotyping data renew knowledge on genetic diversity of a worldwide alfalfa collection and give insights on genetic control of phenology traits

Abstract

China’s and Europe’s dependence on imported protein is a threat to the food self-sufficiency of these regions. It could be solved by growing more legumes, including alfalfa that is the highest protein producer under temperate climate. To create productive and high-value varieties, the use of large genetic diversity combined with genomic evaluation could improve current breeding programs. To study alfalfa diversity, we have used a set of 395 alfalfa accessions (i.e. populations), mainly from Europe, North and South America and China, with fall dormancy ranging from 3 to 7 on a scale of 11. Five breeders provided materials (617 accessions) that were compared to the 400 accessions. All accessions were genotyped using Genotyping-by-Sequencing (GBS) to obtain SNP allele frequency. These genomic data were used to describe genetic diversity and identify genetic groups. The accessions were phenotyped for phenology traits (fall dormancy and flowering date) at two locations (Lusignan in France, Novi Sad in Serbia) from 2018 to 2021. The QTL were detected by a Multi-Locus Mixed Model (mlmm). Subsequently, the quality of the genomic prediction for each trait was assessed. Cross-validation was used to assess the quality of prediction by testing GBLUP, Bayesian Ridge Regression (BRR), and Bayesian Lasso methods. A genetic structure with seven groups was found. Most of these groups were related to the geographical origin of the accessions and showed that European and American material is genetically distinct from Chinese material. Several QTL associated with fall dormancy were found and most of these were linked to genes. In our study, the infinitesimal methods showed a higher prediction quality than the Bayesian Lasso, and the genomic prediction achieved high (>0.75) predicting abilities in some cases. Our results are encouraging for alfalfa breeding by showing that it is possible to achieve high genomic prediction quality.

Genome-wide association study for yield-related traits in faba bean (Vicia faba L.)

“…Low LD in faba bean was previously reported ( Skovbjerg et al., 2023 ; Zhang et al., 2023 ) when comparing different faba bean diversity panels. Thus, the EUCLEG collection was the one showing lower LD blocks (higher recombination), an expected outcome in outbreeding species with high genetic diversity ( Pégard et al., 2023 ; Skovbjerg et al., 2023 ; Zanotto et al., 2023 ; Zhang et al., 2023 ). The rapid LD decay observed in this study (within roughly 150 kbp) revealed the variation present in a highly allogamous panmictic population and suggested that the MTAs identified (or the closely linked genes on either flanking side of the significant SNPs) were nearly or completely independent from each other and, hence, were sufficient for association mapping in faba bean.…”

“…Compared with family-based QTL mapping, GWAS significantly increases mapping resolution and enables minor effect genes to be detected ( Abdurakhmonov et al., 2008 ). In recent years, GWAS studies have been reported in a range of legume crops such as soybean ( Hwang et al., 2014 ), pigeon pea ( Varshney et al., 2017 ), common bean ( Raggi et al., 2019 ), chickpea ( Varshney et al., 2019 ), red clover ( Zanotto et al., 2023 ), alfalfa ( Pégard et al., 2023 ), and the model legume Medicago truncatula ( Bonhomme et al., 2014 ). In faba bean, only a few GWAS studies have been reported so far which have identified candidate genes associated with frost tolerance ( Sallam et al., 2016 ); resistance to Ascochyta fabae ( Faridi et al., 2021 ); tolerance to herbicides ( Abou-Khater et al., 2022 ); drought, heat, and freezing tolerance ( Ali et al., 2016 ; Maalouf et al., 2022 ; Gutiérrez et al., 2023 ); agronomic traits ( Skovbjerg et al., 2023 ); and seed size ( Jayakodi et al., 2023 ).…”

Including marker x environment interactions improves genomic prediction in red clover (Trifolium pratense L.)

“…In breeding populations, PAs were found to vary between 0.21 – 0.66 depending on breeding cycle and location ( Li et al., 2015 ). Recently, Pégard et al. (2023) reported a mean PA of 0.65 for DMY from two locations, France and Serbia, and 0.41 for DOF in a diverse panel of alfalfa accessions, where the marker data also were obtained as allele frequencies derived from sequencing of pooled plant material representing each accession.…”

“…It has been used in ryegrass for prediction of flowering time, crown rust resistance, seed yield and fructan content ( Byrne et al., 2013 ; Fè et al., 2016 ; Cericola et al., 2018 ). Recently, GP was also successfully explored in alfalfa using pooled samples for genotyping ( Pégard et al., 2023 ). PA was above 0.75 for plant height and dormancy in some years, demonstrating the potential of GP for some traits.…”